Regional Fingerprint Liveness Detection Systems and Methods

ABSTRACT

Various systems, methods, and programs embodied in computer-readable mediums are provided for fingerprint liveness detection. In one embodiment, a method for determining fingerprint liveness is provided that comprises receiving a fingerprint image; extracting a region of interest from the fingerprint image; generating a histogram of the region of interest; determining a statistical feature of the histogram; and determining liveness of the fingerprint image based upon the statistical feature.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to copending U.S. provisionalapplications entitled “FINGERPRINT LIVENESS DETECTION USING LOCAL RIDGEFREQUENCIES AND MULTIRESOLUTION TEXTURE ANALYSIS TECHNIQUES” having Ser.No. 60/850,664, filed Oct. 10, 2006, and “NEW WAVELET-BASED FINGERPRINTLIVENESS METHOD” having Ser. No. 60/919,043, filed Mar. 3, 2007, both ofwhich are entirely incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The U.S. government may have a paid-up license in this invention and theright in limited circumstances to require the patent owner to licenseothers on reasonable terms as provided for by the terms of Grant Nos.0325333 and 0325640 awarded by the National Science Foundation,Information Technology Research.

BACKGROUND

Biometric systems are emerging technologies that enable theauthentication of an individual based on physiological or behavioralcharacteristics. Biometric techniques include recognizing faces,fingerprints, hand geometry, palms, voices, gait, irises, signature,etc. Among these biometric identifiers, fingerprint recognition isconsidered the most popular and efficient technique. However, afingerprint sensor system is subject to various threats such as attacksat the sensor level, replay attacks on the data communication stream,and attacks on the database. A variety of fingerprint sensors may bespoofed through fake fingers using moldable plastic, clay, Play-Doh, waxor gelatin. From a security and accountability perspective, a biometricsystem should have the ability to detect when fake biometric samples arepresented.

SUMMARY

Embodiments of the present disclosure methods and systems related tomulti-resolutional texture analysis fingerprint liveness.

Briefly described, one embodiment, among others, comprises a method fordetermining fingerprint liveness. The method comprises receiving afingerprint image; extracting a region of interest from the fingerprintimage; generating a histogram of the region of interest; determining astatistical feature of the histogram; and determining liveness of thefingerprint image based upon the statistical feature. Anotherembodiment, among others, comprises a system. The system comprises aprocessor circuit having a processor and a memory and a detection systemstored in the memory and executable by the processor. The detectionsystem comprises logic that receives a fingerprint image; logic thatextracts a region of interest from the fingerprint image; logic thatgenerates a histogram of the region of interest; logic that determines astatistical feature of the histogram; and logic that determines livenessof the fingerprint image based upon the statistical feature.

Another embodiment, among others, comprises a system. The systemcomprises means for receiving a fingerprint image; means for extractinga region of interest from the fingerprint image; means for generating ahistogram of the region of interest; means for determining a statisticalfeature of the histogram; and means for determining liveness of thefingerprint image based upon the statistical feature.

Other systems, apparatus, methods, features, and advantages of thepresent disclosure will be or become apparent to one with skill in theart upon examination of the following drawings and detailed description.It is intended that all such additional systems, apparatus, methods,features, and advantages be included within this description, be withinthe scope of the present disclosure, and be protected by theaccompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the invention can be better understood with reference tothe following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present invention. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is an illustration of live, synthetic, and dismemberedfingerprint images obtained using capacitive DC, optical, andopto-electric sensing technologies;

FIG. 2 is an illustration of sectioning a fingerprint image of FIG. 1into blocks according to an embodiment of the present invention;

FIG. 3 is an illustration of a classifier to determine liveness of afingerprint image such as those illustrated in FIG. 1 according to anembodiment of the present invention;

FIG. 4 is a flow chart that provides one example of the operation of asystem to determine liveness of fingerprint images such as thoseillustrated in FIG. 1 according to an embodiment of the presentinvention;

FIG. 5 is a schematic block diagram of one example of a system employedto detect fingerprint liveness and to perform various analysis withrespect to the fingerprint liveness detection according to an embodimentof the present invention;

FIG. 6 is a graphical representation of fuzzy clusters resulting fromtraining with data derived from fingerprint images such as thoseillustrated in FIG. 1 according to an embodiment of the presentinvention;

FIG. 7 is an illustration of live and synthetic fingerprint imagesobtained using an optical scanner according to an embodiment of thepresent invention;

FIG. 8 illustrates Region of Interest (ROI) processing of a live imagefrom FIG. 7 according to an embodiment of the present invention;

FIG. 9 is a flow chart 900 that provides one example of the operation ofa system to determine liveness of fingerprint images, such as thoseillustrated in FIG. 7, utilizing ROI processing according to anembodiment of the present invention;

FIG. 10 is a schematic block diagram of one example of a system employedto detect fingerprint liveness and to perform various analysis withrespect to the fingerprint liveness detection according to an embodimentof the present invention;

FIG. 11 illustrates typical ROIs of live and synthetic fingerprintimages from FIG. 7 and their corresponding intensity histogramdistributions according to an embodiment of the present invention;

FIG. 12 illustrates the comparison of features derived from the ROIhistograms of all live and synthetic fingerprint images according to anembodiment of the present invention;

FIG. 13 is an illustration of an initial (0 sec) fingerprint image and asubsequent (2 sec) fingerprint image obtained from a “live” captureaccording to an embodiment of the present invention;

FIG. 14 is an illustration of demonstrating the linking betweensingularity points of the initial image and subsequent image of FIG. 13according to an embodiment of the present invention;

FIG. 15 is a flow chart that provides one example of the operation of asystem to determine liveness of fingerprint images such as thoseillustrated in FIG. 13 according to an embodiment of the presentinvention according to an embodiment of the present invention; and

FIG. 16 a schematic block diagram of one example of a system employed todetect fingerprint liveness and to perform various analysis with respectto the fingerprint liveness detection according to an embodiment of thepresent invention.

FIG. 17 illustrates EMD decomposition of live and synthetic fingerprintsaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

Disclosed herein are various embodiments of methods related tomulti-resolutional texture analysis fingerprint liveness. Reference willnow be made in detail to the description of the embodiments asillustrated in the drawings, wherein like reference numbers indicatelike parts throughout the several views.

Biometric systems are emerging technologies that enable theidentification and verification of an individual based uponphysiological or behavioral characteristics. Biometric identifiers arereplacing traditional identifiers, as it is difficult to steal, replace,forget or transfer them. Fingerprint recognition systems (FRS) are amongthe oldest and most popular biometric authentication systems. Automaticfingerprint matching involves determining the degree of similaritybetween two fingerprint impressions by comparing their ridge structuresand/or the spatial distributions of their minutiae points. However, itmay be possible to spoof different fingerprint technologies by means ofrelatively crude and inexpensive methods.

A fingerprint sensor system may be subject to various threats likeattacks at the sensor level using artificial, or in the extreme,dismembered fingers. It may be possible to spoof a variety offingerprint sensors with a dismembered finger or a well-duplicatedsynthetic finger. Spoofing is the fraudulent entry of unauthorizedpersonnel through a fingerprint recognition system by using a fauxfingerprint sample. Spoofing techniques can utilize synthetic fingersmade of materials including, but not limited to, gelatin (gummyfingers), moldable plastic, clay, Play-Doh, wax, and silicon. FIG. 1 isan illustration of live, artificial (i.e., Play-Doh and gummy), anddismembered (i.e., cadaver) fingerprint images obtained using capacitiveDC, optical, and opto-electric sensing technologies. The syntheticfingers may be developed from casts of live fingers or latentfingerprints, which were obtained using dental or other castingmaterials.

Liveness detection is an anti-spoofing method that assist thefingerprint scanner in determining whether the introduced biometric iscoming from a live source. Liveness detection in a fingerprint biometricsystem can include, but is not limited to, measuring skin resistance,temperature, pulse oximetry, and electrocardiogram (ECG). Analysis ofridge frequencies, intensity, and/or texture in fingerprint images canalso be used as detection methods for liveness. In addition,perspiration patterns in fingerprint images can be used to detectliveness. Unlike artificial and cadaver fingers, live fingers have adistinctive spatial perspiration phenomenon both statistically anddynamically.

Function Analysis

One embodiment, among others, of liveness detection comprisesclassification analysis based upon features of a fingerprint image.Fingerprint image features (or functions) can be derived from an imageusing multi-resolution texture analysis and/or inter-ridge frequencyanalysis. Other information derived from the image, such as ridgereliability, may also be utilized. Classification analysis, such asFuzzy c-means classifier analysis, evaluates the features/functions todetermine the image type. Multi-resolution texture analysis techniquesutilize the grayscale of the fingerprint image to develop first and/orsecond order features related to the image. The gray level associatedwith the pixels of a fingerprint image can be used to analyze theliveness associated with that fingerprint image. For an 8-bit grayscaleimage, there are 256 (0-255) different possible levels (or bins) foreach pixel. Other grayscales would provide different levels ofresolution. A histogram will graphically display the distribution ofpixels (y-axis) among the possible grayscale values (x-axis). Visuallyit is very pertinent that gray level distribution in a fingerprint imagechanges when the physical structure changes. Several texture featuresare used to quantify this information. Texture features can be dividedinto first, second and higher order statistical features. The grayscaledistribution of the single pixels is modeled as first order statisticswhile second order statistics refer to the joint grayscale functionbetween pairs of pixels.

First order features refer to the visually observed differences between“live” and “not live” fingerprints illustrated in FIG. 1. This can beconfirmed by changes in the histograms of different fingerprints. IfH(n) indicates the normalized histogram, the first order features caninclude the following:

$\begin{matrix}{{{{{Energy}\text{:}\mspace{14mu} e} = {\sum\limits_{n = 0}^{N - 1}\; {H(n)}^{2}}};}\;} & \left( {{EQN}.\mspace{14mu} 1} \right) \\{{{{Entropy}\text{:}\mspace{14mu} s} = {\sum\limits_{n = 0}^{N - 1}\; {{H(n)}\log \; {H(n)}}}};} & \left. \left( {{EQN}.\mspace{14mu} 2} \right) \right) \\{{{{Median}\text{:}\mspace{14mu} M} = {\arg \; {\min\limits_{a}\left( {\sum\limits_{n}\; {{H(n)}{{n - a}}}} \right)}}};} & \left( {{EQN}.\mspace{14mu} 3} \right) \\{{{{Variance}\text{:}\mspace{14mu} \sigma^{2}} = {\sum\limits_{n = 0}^{N}\; {\left( {n - \mu} \right)^{2}{H(n)}}}};} & \left( {{EQN}.\mspace{14mu} 4} \right) \\{{{{Skewness}\text{:}\mspace{14mu} \gamma_{1}} = {\frac{1}{\sigma^{3}}{\sum\limits_{n = 0}^{N - 1}\; {\left( {n - \mu} \right)^{3}{H(n)}}}}};} & \left( {{EQN}.\mspace{14mu} 5} \right) \\{{{{{Kurtosis}\text{:}\mspace{14mu} \gamma_{2}} = {\frac{1}{\sigma^{4}}{\sum\limits_{n = 0}^{N - 1}\; {\left( {n - \mu} \right)^{4}{H(n)}}}}};}{and}} & \left( {{EQN}.\mspace{14mu} 6} \right) \\{{{{Coefficient}\mspace{14mu} {of}\mspace{14mu} {Variation}\text{:}\mspace{14mu} {cv}} = \frac{\sigma}{\mu}};} & \left( {{EQN}.\mspace{14mu} 7} \right)\end{matrix}$

where N is equal to the number of bins (e.g., 256 for an 8-bitgreyscale) and μ and σ are the mean and the standard deviation,respectively.

Multi-resolution texture analysis techniques can also utilize secondorder features that represent second order statistics of the fingerprintimage. A co-occurrence matrix (Υ), which is essentially the jointprobability function of two image elements in a given direction anddistance, Υ(n, m|d, θ), is calculated. Symmetric directions θ=0°, 45°,90°, 135° can be used to avoid dependency on the direction. Based uponthe definition of the co-occurrence matrix, second order features, suchas the following, can be determined:

$\begin{matrix}{{{{{Cluster}\mspace{14mu} {Shade}\text{:}\mspace{14mu} {CS}} = \frac{\begin{matrix}{\sum\limits_{n = 0}^{N}{\sum\limits_{m = 0}^{N}\begin{pmatrix}{n - \mu_{x} +} \\{m - \mu_{y}}\end{pmatrix}^{3}}} \\{\mathrm{\Upsilon}\left( {n,m} \right)}\end{matrix}}{\left( {\sigma_{x}^{2} + \sigma_{y}^{2} + {2{\rho\sigma}_{x}\sigma_{y}}} \right)^{3/2}}};}{and}} & \left( {{EQN}.\mspace{14mu} 8} \right) \\{{{Cluster}\mspace{14mu} {Prominence}\text{:}\mspace{14mu} {CP}} = {\frac{\begin{matrix}{\sum\limits_{n = 0}^{N}{\sum\limits_{m = 0}^{N}\begin{pmatrix}{n - \mu_{x} +} \\{m - \mu_{y}}\end{pmatrix}^{4}}} \\{\mathrm{\Upsilon}\left( {n,m} \right)}\end{matrix}}{\left( {{\sigma_{x}^{2}\sigma_{y}^{2}} + {2{\rho\sigma}_{x}\sigma_{y}}} \right)^{2}}.}} & \left( {{EQN}.\mspace{14mu} 9} \right)\end{matrix}$

Multi-resolution second order texture analysis is performed usingbi-orthogonal wavelets. Other methods for performing time-frequencyanalysis include, but are not limited to, wavelet packets, Daubechieswavelets, Harr wavelets, and short-time Fourier transforms. Abi-orthogonal pyramid of the fingerprint image is computed and thenco-occurrence matrix Υ is calculated using fixed distance d. Aco-occurrence matrix may be calculated for a single level or for eachlevel of the pyramid.

Fingerprint local-ridge frequency analysis is also performed on thefingerprint image. FIG. 2 is an illustration of sectioning a fingerprintimage of FIG. 1 into blocks according to an embodiment of the presentinvention. The image is divided into blocks of size w×w, each centeredat (i, j). The local-ridge frequency can be calculated for each imageblock as:

$\begin{matrix}\begin{matrix}\begin{matrix}{{{\Gamma \left( {i,j} \right)} = \frac{{\sum\limits_{u = {{- w_{\gamma}}/2}}^{w_{\gamma}/2}{\sum\limits_{v = {{- w_{\gamma}}/2}}^{w_{\gamma}/2}{{\Psi_{g}\left( {u,v} \right)}\mu \left( {\Gamma \left( {{i - {uw}},{j - {vw}}} \right)} \right)}}}\mspace{11mu}}{{\sum\limits_{u = {{- w_{\gamma}}/2}}^{w_{\gamma}/2}{\sum\limits_{v = {{- w_{\gamma}}/2}}^{w_{\gamma}/2}{{\Psi_{g}\left( {u,v} \right)}{\delta \left( {\Gamma \left( {{i - {uw}},{j - {vw}}} \right)} \right)}}}}\mspace{11mu}}}\;} \\{where} \\{\mspace{169mu} {{{\mu (x)} = \begin{matrix}0 & {{{if}\mspace{14mu} x} \leq 0} \\x & {otherwise}\end{matrix}};}}\end{matrix} \\{{{\delta (x)} = \begin{matrix}0 & {{{if}\mspace{14mu} x} \leq 0} \\1 & {otherwise}\end{matrix}};}\end{matrix} & \left( {{EQN}.\mspace{14mu} 10} \right)\end{matrix}$

Ψ_(g) is a bi-orthogonal kernel; and w_(γ) is the kernel size.

Because of the scalable nature of the wavelets, Γ(i, j) is alsoscalable. For the blocks of the fingerprint image where inter-ridgedistance hardly changes, the analysis results in high energy in the LLbands, the coarsest level of the pyramids, and can be ignored duringsynthesis of the wavelet pyramid.

Statistical analysis of local ridge frequencies allows determination offeatures including, but not limited to, the following:

-   -   ridge mean (μ_(r));    -   ridge median (M_(r));    -   ridge variance (γ_(r)); and    -   ridge standard deviation (σ_(r)).

The fingerprint image may contain many artifacts, and hence orientationfields, generated from the fingerprint images that can not be directlyused. Ridge reliability can be calculated using alignment of the ridgewith the other adjacent ridges and the length of the connected ridgesegments.

FIG. 3 is an illustration of a classifier to determine liveness of afingerprint image such as those illustrated in FIG. 1 according to anembodiment of the present invention. Image analysis data 310 issubmitted to fuzzy classifier 320. Image analysis data 310 can include,but is not limited to, data derived through multi-resolution textureanalysis and/or local-ridge frequency analysis. Data 310 can include,but is not limited to, first order features such as energy, entropy,median, variance, skewness, and Kurtosis; second order features such ascluster shade and cluster prominence; local-ridge data such as ridgemean, ridge median, ridge variance, ridge standard deviation, and ridgereliability.

Image analysis data 310 is initially condensed. Condensation caninclude, but is not limited to, reduction in the dimension of the dataspace, filtering of the data, and reducing the data for presentation toa classifier. In the embodiment of FIG. 3, the image analysis data 310is initially processed using principal component analysis (PCA) 330.Principal component analysis is a technique used to reducemultidimensional data sets to lower dimensions for analysis. PCA ismathematically defined as an orthogonal linear transformation thattransforms the data to a new coordinate system such that the greatestvariance by any projection of the data comes to lie on the firstprincipal component, the second greatest variance on the secondcoordinate, and so on. PCA can be used for dimensionality reduction in adata set by retaining those characteristics of the data set thatcontribute most to its variance by keeping lower-order principalcomponents while eliminating higher-order components.

PCA 330 is used to condense the submitted multidimensional data sets foranalysis by reducing the data set dimension. Other methods that may alsobe used to reduce the data submitted to the fuzzy classifier include,but are not limited to, neural networks, classification trees, selectionof most statistically different features, and modification trees.Training of the PCA 330, or other reduction methods, is facilitated by aset of known training data.

The reduced feature set is provided to the fuzzy classifier 320. Thereduced set is analyzed by the fuzzy classifier 320 to determineliveness 340 of the fingerprint image. In the current embodiment, fuzzyc-means clustering is utilized. Other classification methods including,but not limited to, neural networks, nearest neighbor classifiers,support vector machines, and classification trees may also be used todetermine liveness 340. After the analysis is complete, classifier 320indicates if the fingerprint image is “live” or “not live” 330. Fuzzyc-means clustering (FCM) differs from hard k-means in that it does notemploy a hard threshold. Instead, FCM employs fuzzy partitioning suchthat a data point can belong to some or all groups with differentmembership grades between 0 and 1. This improves the sensitivity of theliveness detection. An iterative training approach to find clustercenters (i.e., centroids) that minimize a dissimilarity function may beadopted. The dissimilarity function is given as:

$\begin{matrix}{{J\left( {U,{c_{1}c_{2}},\ldots \mspace{14mu},c_{c}} \right)} = {{\sum\limits_{i = 1}^{c}J_{i}} = {\sum\limits_{i = 1}^{c}{\sum\limits_{j = 1}^{n}{u_{ij}^{m}d_{ij}^{2}}}}}} & \left( {{EQN}.\mspace{14mu} 11} \right)\end{matrix}$

where, U is the membership matrix, c_(i) is the centroid of cluster i,u_(ij) is a membership grade between 0 and 1, d_(ij) is the Euclideandistance between the i^(th) centroid (c_(i)) and the j^(th) data point(x_(j)), and mε[1, ∞] is a weighting exponent. The membership matrix (U)is randomly initialized using:

$\begin{matrix}{{{\sum\limits_{i = 1}^{c}u_{ij}} = 1},{{\forall j} = 1},\ldots \mspace{14mu},{n.}} & \left( {{EQN}.\mspace{14mu} 12} \right)\end{matrix}$

The following conditions are used to iteratively minimize thedissimilarity function. One skilled in the art would understand thatother minimization methods could also be used. First, the centroids(c_(i)) are calculated using:

$\begin{matrix}{c_{i} = {\frac{\sum\limits_{j = 1}^{n}{u_{ij}^{m}x_{j}}}{\sum\limits_{j = 1}^{n}u_{ij}^{m}}.}} & \left( {{EQN}.\mspace{14mu} 13} \right)\end{matrix}$

The membership grade for each data point is then updated using:

$\begin{matrix}{u_{ij} = {\frac{1}{\sum\limits_{k = 1}^{c}\frac{d_{ij}^{2/{({m - 1})}}}{d_{kj}}}.}} & \left( {{EQN}.\mspace{14mu} 14} \right)\end{matrix}$

By iteratively updating the cluster centers and the membership gradesfor each data point, FCM iteratively moves the cluster centers withindata set. Dissimilarity between two clusters is calculated using EQN.11. The iterative training process is stopped when the differencebetween the current dissimilarity and the previous dissimilarity isbelow a threshold. The threshold my be a fixed predefined value or varyover the iteration process.

After training is completed, liveness can be determined based uponcalculated distance to the cluster centroid of each classification.Image analysis data 310 (FIG. 3) from a new fingerprint image issubmitted to the classifier 320 where the data is reduced by PCA 330.The distance to the “live” and “not live” cluster centroids is thendetermined. The fingerprint liveness 340 is associated with the closestcluster centroid. In other embodiments, liveness may be associated withthe cluster with the highest probability of membership. A minimumprobability threshold may be used to ensure reliability of theclassification. In another embodiment, liveness is determined based uponimage data being within a predetermined cluster contour.

Referring next to FIG. 4, shown is a flow chart 400 that provides oneexample of the operation of a system to determine liveness offingerprint images such as those illustrated in FIG. 1 according to anembodiment of the present invention. Alternatively, the flow chart ofFIG. 4 may be viewed as depicting steps of an example of a methodimplemented in a processor system 500 (FIG. 5) to determine liveness offingerprint images as set forth above. The functionality of thefingerprint liveness detection as depicted by the example flow chart ofFIG. 4 may be implemented, for example, in an object oriented design orin some other programming architecture. Assuming the functionality isimplemented in an object oriented design, then each block representsfunctionality that may be implemented in one or more methods that areencapsulated in one or more objects. The fingerprint liveness detectionmay be implemented using any one of a number of programming languagessuch as, for example, C, C++, or other programming languages.

Beginning with block 410, a fingerprint image is received. Functionsrelated to the received image are determined using one or more methodssuch as, but not limited to, multi-resolution texture analysis andlocal-ridge frequency analysis techniques.

Multi-resolution texture analysis techniques utilize the grayscale ofthe fingerprint image to develop multi-resolution texture analysis dataincluding, but not limited to, first and/or second order featuresrelated to the image. A histogram of the grayscale distribution of theimage pixels is determined in block 420. In block 425, the histogram canbe used to determine first order functions of the image including, butnot limited to, energy, entropy, median, variance, skewness, andKurtosis. Second order functions of the image may also be determined(block 435) based upon a co-occurrence matrix determined in block 430.Second order functions can include, but are not limited to, clustershade and cluster prominence. Higher order functions may also beutilized to determine liveness.

Local-ridge frequency analysis determines local-ridge frequency data(block 440) for each block of the fingerprint image. Statisticalfunctions are determined in block 445 from the local-ridge data. Thestatistical functions (or data) can include, but are not limited to,ridge mean, ridge median, ridge variance, ridge standard deviation.Ridge reliability can also be determined from the local-ridgeinformation.

The analysis data determined from the fingerprint image may be condensedin block 450. Condensation can include, but is not limited to, reductionin the dimension of the data space, filtering of the data, and reducingthe data for presentation to a classifier. Principal component analysis(PCA), neural networks, and modification trees can be used to reduce thesubmitted data set for classification. In one embodiment, among others,PCA reduces the multidimensional analysis data set to lower dimensionsby keeping lower-order principal components while eliminatinghigher-order components. One skilled in the art would understand thatcondensation of a limited data set may not be necessary forclassification. The condensed data set is classified using a fuzzyclassifier in block 460 and liveness of the fingerprint image isdetermined in block 470 based upon proximity to a classification clustercentroid.

Referring next to FIG. 5, shown is one example of a system that performsvarious functions using fingerprint liveness detection according to thevarious embodiments as set forth above. As shown, a processor system 500is provided that includes a processor 503 and a memory 506, both ofwhich are coupled to a local interface 509. The local interface 509 maybe, for example, a data bus with an accompanying control/address bus ascan be appreciated by those with ordinary skill in the art. Theprocessor system 500 may comprise, for example, a computer system suchas a server, desktop computer, laptop, personal digital assistant, orother system with like capability.

Coupled to the processor system 500 are various peripheral devices suchas, for example, a display device 513, a keyboard 519, and a mouse 523.In addition, other peripheral devices that allow for the capture ofvarious patterns may be coupled to the processor system 500 such as, forexample, an image capture device 526, or a biometric input device 529.The image capture device 526 may comprise, for example, a digital cameraor other such device that generates images that comprise patterns to beanalyzed as described above. Also, the biometric input device 529 maycomprise, for example, a fingerprint input device, optical scanner, orother biometric device 529 as can be appreciated.

Stored in the memory 506 and executed by the processor 503 are variouscomponents that provide various functionality according to the variousembodiments of the present invention. In the example embodiment shown,stored in the memory 506 is an operating system 553 and a fingerprintliveness detection system 556. In addition, stored in the memory 506 arevarious images 559 and various classification clusters 563. Theclassification clusters 563 may be associated with a corresponding imagecapture device 526 or biometric input device 529. The images 559 and theclassification clusters 563 may be stored in a database to be accessedby the other systems as needed. The images 559 may comprise fingerprintssuch as the images in FIG. 1 or other patterns as can be appreciated.The images 559 comprise, for example, a digital representation ofphysical patterns or digital information such as data, etc.

The fingerprint liveness detection system 556 is executed by theprocessor 503 in order to classify whether a fingerprint image is “live”or “not live” as described above. A number of software components arestored in the memory 506 and are executable by the processor 503. Inthis respect, the term “executable” means a program file that is in aform that can ultimately be run by the processor 503. Examples ofexecutable programs may be, for example, a compiled program that can betranslated into machine code in a format that can be loaded into arandom access portion of the memory 506 and run by the processor 503, orsource code that may be expressed in proper format such as object codethat is capable of being loaded into a of random access portion of thememory 506 and executed by the processor 503, etc. An executable programmay be stored in any portion or component of the memory 506 including,for example, random access memory, read-only memory, a hard drive,compact disk (CD), floppy disk, or other memory components.

The memory 506 is defined herein as both volatile and nonvolatile memoryand data storage components. Volatile components are those that do notretain data values upon loss of power. Nonvolatile components are thosethat retain data upon a loss of power. Thus, the memory 506 maycomprise, for example, random access memory (RAM), read-only memory(ROM), hard disk drives, floppy disks accessed via an associated floppydisk drive, compact discs accessed via a compact disc drive, magnetictapes accessed via an appropriate tape drive, and/or other memorycomponents, or a combination of any two or more of these memorycomponents. In addition, the RAM may comprise, for example, staticrandom access memory (SRAM), dynamic random access memory (DRAM), ormagnetic random access memory (MRAM) and other such devices. The ROM maycomprise, for example, a programmable read-only memory (PROM), anerasable programmable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), or other like memory device.

The processor 503 may represent multiple processors and the memory 506may represent multiple memories that operate in parallel. In such acase, the local interface 509 may be an appropriate network thatfacilitates communication between any two of the multiple processors,between any processor and any one of the memories, or between any two ofthe memories etc. The processor 503 may be of electrical, optical, ormolecular construction, or of some other construction as can beappreciated by those with ordinary skill in the art.

The operating system 553 is executed to control the allocation and usageof hardware resources such as the memory, processing time and peripheraldevices in the processor system 500. In this manner, the operatingsystem 553 serves as the foundation on which applications depend as isgenerally known by those with ordinary skill in the art.

Experimental Validation

A previously collected data set was used to confirm the disclosedidentification process. This data set was diverse in age, sex, andethnicity by being composed of a variety of age and ethnic groups andapproximately equal numbers of men and women. The data was collectedusing three different fingerprint scanners, with different underlyingtechnologies, Secugen (model FDU01), Ethentica (model Ethenticator USB2500) and Precise Biometrics (model PS100), which utilize optical,electro-optical and capacitive DC techniques of capturing fingerprintimages, respectively.

To provide synthetic fingerprint images, a cast was made from each livesubject using dental material. Synthetic digits were created fromPlay-Doh using the procedure given in “Determination of vitality from anon-invasive biomedical measurement for use in fingerprint scanners,” R.Derakshani, S. Schuckers, L. Hornak, and L. Gorman, Pattern RecognitionJournal, vol. 36, no. 2, 2003, which is hereby incorporated by referencein its entirety. Images from cadaver subjects were collected incollaboration with MUSCULOSKELETAL Research Center at West VirginiaUniversity (WVU). Protocols for data collection from the subjects werefollowed that were approved by the West Virginia UniversityInstitutional Review Board (IRB) (HS#14517 and HS#15322). The data setconsisted of 130 to 136 fingerprints from live, synthetic, and cadaversources, depending on the scanner used. TABLE 1 summarizes the availabledata. Differences in the number of images per scanner were caused byinclusion criteria, which requires an accepted enrollment image withinfive tries. Image collection was performed using custom softwaredeveloped from device software development kits. The process wasimplemented using MATLAB.

TABLE 1 Capacitive DC Electro-Optical Optical (Precise Biometric)(Ethentica) (Secugen) Live 58 55 58 Synthetic 50 50 52 Cadaver 33 22 28

Training of the PCA and FCM was performed separately for all the threescanners. A total of 35 live and 35 synthetic images were used fortraining. In addition, the number of cadaver images used for trainingwere 15, 12 and 15 from Precise Biometrics, Ethentica and Secugen,respectively.

FIG. 6 is a graphical representation of fuzzy clusters resulting fromtraining with data derived from fingerprint images such as thoseillustrated in FIG. 1 according to an embodiment of the presentinvention. The training data, cluster centroids and contours wereplotted on the plane formed by the first two principle components usinga non-linear sammom mapping. The cluster on the left (i.e., negativevalues) includes “live” data while the cluster on the right side (i.e.,positive values) includes “not live” data. The remaining fingerprintimages were used to confirm the process. Image analysis data 310 (FIG.3) was determined for each fingerprint image using multi-resolutiontexture analysis, local-ridge frequency analysis, and ridge reliability.Image analysis data 310 received by the trained classifier 320 includedfirst order features such as energy, entropy, median, variance,skewness, and Kurtosis; second order features such as cluster shade andcluster prominence; local ridge data such as ridge mean, ridge median,ridge variance, ridge standard deviation, and ridge reliability. Thesubmitted data 310 was processed by the PCA 330 and fuzzy classifier320. Classification 340 was based upon distance to the closest clustercentroid. Using this method, classification rates of 97.3%, 96.5% and92.3% were achieved using the process for Secugen, Precise Biometricsand Ethentica, respectively.

Intensity Analysis

One embodiment, among others, of liveness detection comprisesclassification analysis based upon features of fingerprint imageintensity variation caused by moisture variability and differencesbetween human skin and synthetic materials such as Play-Doh, gelatin andsilicone. FIG. 7 is an illustration of live and synthetic (i.e.,Play-Doh, gelatin, and silicone) fingerprint images obtained using anoptical scanner according to an embodiment of the present invention. Ascan be observed, intensity and uniformity of the live and syntheticimages vary based upon the source.

A captured fingerprint image may also be affected by cleanliness ofeither the scanner surface (e.g., latent fingerprint impressionsdeposited on the surface) or the fingerprint itself. A median filter canbe used to remove this kind of noise from the scanned image.

To improve the classification process, a region of interest (ROI) may beisolated for further processing. To determine the ROI, the whole imageis initially divided into blocks of small size (e.g., 16×16 pixels) andthe variance of each block is computed. In one embodiment, variation inintensity of pixels within a block is computed. Other variations caninclude, but are not limited to, the ratio of light to dark pixels basedupon a predefined threshold. If the variance of a block is less than athreshold value, then the block is removed from original image. This iscontinued until each block has been evaluated. Next, the center of thefingerprint image is determined. The fingerprint foreground is segmentedand the total area calculated. One segmentation method is described in“Fingerprint Image Enhancement: Algorithm and Performance Evaluation,”L. Hong, Y. Wang, and A. Jian, IEEE Trans. on Pattern Analysis andMachine Intelligence, vol. 20, no. 8, pp. 777-789, August 1998, which ishereby incorporated by reference in its entirety. Then, the accumulativerow numbers and column numbers are averaged by the total area to get acenter point of the image. Other appropriate methods for determining animage center may also be used. Finally, the ROI is extracted from theprocessed image by cropping a predefined area (e.g., 100×100 squarearea) around the center of the fingerprint. Other areas can include, butare not limited to, rectangles, hexagons and octagons.

FIG. 8 illustrates ROI processing of a live image from FIG. 7 accordingto an embodiment of the present invention. The initial fingerprint image810 is shown on the left. The processing image is illustrated on theright. Blocks that did not meet the variance threshold 820 have beenremoved (i.e., blacked out) on the edges of the processed image. Thecenter of the fingerprint 830 was determined and the selected area 840about center 830 is extracted. The center portion 840 of the fingerprintimage 810 is selected to allow for reduced data processing whileproviding sufficient information to detect the liveness of the image.

After extracting the centered ROI, intensity of the fingerprint iscompared using histogram distribution analysis. The histogram of animage indicates the number of pixels in an image at each differentintensity (or grayscale) value. For an 8-bit grayscale image, there are256 different possible intensities (i.e., 0-255, where 0 is black and255 is white). Other grayscales (e.g., 16- or 32-bit) or color scalesmay be utilized. Thus, the associated histogram will graphically displaythe distribution of pixels among those grayscale values.

If H(i) indicates the histogram distribution function along thegrayscale levels, the following statistical features are used toquantify the difference between the extracted images:

$\begin{matrix}{{A = \frac{\sum\limits_{i = 0}^{120}{H(i)}}{\sum\limits_{i = 0}^{255}{H(i)}}};} & \left( {{EQN}.\mspace{14mu} 15} \right) \\{{{B = \frac{\sum\limits_{i = 230}^{244}{H(i)}}{\sum\limits_{i = 0}^{255}{H(i)}}};}{and}} & \left( {{EQN}.\mspace{14mu} 16} \right) \\{C = {\frac{\sum\limits_{i = 250}^{255}{H(i)}}{\sum\limits_{i = 0}^{255}{H(i)}}.}} & \left( {{EQN}.\mspace{14mu} 17} \right)\end{matrix}$

Feature A is used to quantify the percentage of pixels that aredistributed within the dark levels. Feature B is used to quantify thepercentage of pixels that are distributed within the white levels, butnot the whitest level. Feature C is used to quantify the percentage ofpixels that are distributed at the whitest levels.

After the statistical features (A, B, C) are extracted, patternclassification tools such as, but not limited to, neural networks,nearest neighbor analysis, support vector machines, and classification(or decision) trees are used to separate the “not live” subjectsincluding synthetic fingers from the “live” subjects.

Neural networks are pattern classification methods for non-linearproblems. Multilayer perceptrons (MLPs) are feed forward neural networkstrained with the standard back-propagation algorithm. Supervisednetworks are trained to transform data into a desired response. Themultilayer perceptron is capable of learning a rich variety of nonlineardecision surfaces. In one embodiment, a two-layer feed-forward networkcan be created where the inputs are the extracted statistical features(A, B, C). The first hidden layer uses three tansig neurons and thesecond layer has one purelin neuron. The “trainlm” network trainingfunction is used. For convenience of training, bipolar targets (+1 and−1) are chosen to denote “live” and “not live” categories, respectively.One skilled in the art would understand that other types of neuralnetworks could be utilized.

Nearest Neighbor classifier is another supervised statistical patternrecognition method which can be used. Nearest Neighbor classifiers aredescribed in “Instance-based learning algorithms,” D. Aha and D. Kibler,Machine Learning, vol. 6, pp. 37-66, 1991, which is hereby incorporatedby reference in its entirety. This classifier achieves consistently highperformance without a priori assumptions about the distributions fromwhich the training examples are drawn. It utilizes a training set ofboth positive and negative cases. A new sample is classified bycalculating the normalized Euclidean distance to the nearest trainingcase.

Classification trees may also be utilized to classify the image.Classification trees derive a sequence of if-then-else rules using atraining data set in order to assign a class label to the input data.The user can view the decision rules to verify that the rules matchtheir understanding of the classification. Several learning andsearching strategies are utilized to train the models including, but notlimited to, ADTree, J48, Random Forest, and Bagging Tree. ADTreeclassification is described in “The alternating decision tree learningalgorithm,” Y. Freund and L. Mason, Proceeding of the SixteenthInternational Conference on Machine Learning, Bled, Slovenia, pp.124-133, 1999, which is hereby incorporated by reference in itsentirety. J48 classification is described in C4.5: Programs for MachineLearning, R. Quinlan, 1993, which is hereby incorporated by reference inits entirety. Random Forest classification is described in “RandomForests,” L. Breiman, Machine Learning, 45(1):5-32, 2001, which ishereby incorporated by reference in its entirety. Bagging Treeclassification is described in “Bagging Predictors,” L. Breiman, MachineLearning, 24(2):123-140, 1996, which is hereby incorporated by referencein its entirety.

A support vector machine (SVM) may also be used to make theclassification. Support vector machines are described in Fast Trainingof Support Vector Machines using Sequential Minimal Optimization, J.Platt, Chap. 12: Advances in Kernel Methods—Support Vector Learning, pp.185-208, 1999, which is hereby incorporated by reference in itsentirety. Support vector machines are a set of related supervisedlearning methods used for classification. SVMs simultaneously minimizethe empirical classification error and maximize the geometric margin;hence they are also known as maximum margin classifiers. SVMs map inputvectors to a higher dimensional space where a maximal separatinghyperplane is constructed. Two parallel hyperplanes are constructed oneach side of the hyperplane that separates the data. The separatinghyperplane is the hyperplane that maximizes the distance between the twoparallel hyperplanes. The larger the margin or distance between theseparallel hyperplanes, the better the generalization error of theclassifier will be. Classification of new input data is based upon whichplane it is mapped to.

Referring next to FIG. 9, shown is a flow chart 900 that provides oneexample of the operation of a system to determine liveness offingerprint images, such as those illustrated in FIG. 7, utilizing ROIprocessing according to an embodiment of the present invention.Alternatively, the flow chart of FIG. 9 may be viewed as depicting stepsof an example of a method implemented in a processor system 1000 (FIG.10) to determine liveness of fingerprint images as set forth above. Thefunctionality of the fingerprint liveness detection as depicted by theexample flow chart of FIG. 9 may be implemented, for example, in anobject oriented design or in some other programming architecture.Assuming the functionality is implemented in an object oriented design,then each block represents functionality that may be implemented in oneor more methods that are encapsulated in one or more objects. Thefingerprint liveness detection may be implemented using any one of anumber of programming languages such as, for example, C, C++, or otherprogramming languages.

Beginning with block 910, a fingerprint image is received. In block 920,the image is initially processed by removing sections (or blocks) of theimage that exhibit variance below a threshold value. The center of thereduced image is then determined in block 930 and a ROI about the centerof the image is extracted in block 940. The ROI may be a predefined areaor may be determined based upon reduced image area, pixel density, orother appropriate means.

In block 950, a histogram is determined from the extracted ROI.Statistical features, such as, but not limited to, A, B, and C listed inEQNS. 15-17, are determined from the histogram distribution (block 960).One skilled in the art would understand that other features derived fromthe histogram may also be utilized. The statistical features are used todetermine the liveness of the image in block 970 using patternclassification tools including, but not limited to, neural networks,nearest neighbor classifiers, classification trees, and support vectormachines.

Referring next to FIG. 10, shown is one example of a system thatperforms various functions using fingerprint liveness detectionaccording to the various embodiments as set forth above. As shown, aprocessor system 1000 is provided that includes a processor 1003 and amemory 1006, both of which are coupled to a local interface 1009. Thelocal interface 1009 may be, for example, a data bus with anaccompanying control/address bus as can be appreciated by those withordinary skill in the art. The processor system 1000 may comprise, forexample, a computer system such as a server, desktop computer, laptop,personal digital assistant, or other system with like capability.

Coupled to the processor system 1000 are various peripheral devices suchas, for example, a display device 1013, a keyboard 1019, and a mouse1023. In addition, other peripheral devices that allow for the captureof various patterns may be coupled to the processor system 1000 such as,for example, an image capture device 1026, or a biometric input device1029. The image capture device 1026 may comprise, for example, a digitalcamera or other such device that generates images that comprise patternsto be analyzed as described above. Also, the biometric input device 1029may comprise, for example, a fingerprint input device, optical scanner,or other biometric device 1029 as can be appreciated.

Stored in the memory 1006 and executed by the processor 1003 are variouscomponents that provide various functionality according to the variousembodiments of the present invention. In the example embodiment shown,stored in the memory 1006 is an operating system 1053 and a fingerprintliveness detection system 1056. In addition, stored in the memory 1006are various images 1059 and various histograms 1063. The histograms 1063may be associated with corresponding ones of the various images 1059.The images 1059 and the histograms 1063 may be stored in a database tobe accessed by the other systems as needed. The images 1059 may comprisefingerprints such as the images in FIG. 7 or other patterns as can beappreciated. The images 1059 comprise, for example, a digitalrepresentation of physical patterns or digital information such as data,etc.

The fingerprint liveness detection system 1056 is executed by theprocessor 1003 in order to classify whether a fingerprint image is“live” or “not live” as described above. A number of software componentsare stored in the memory 1006 and are executable by the processor 1003.In this respect, the term “executable” means a program file that is in aform that can ultimately be run by the processor 1003. Examples ofexecutable programs may be, for example, a compiled program that can betranslated into machine code in a format that can be loaded into arandom access portion of the memory 1006 and run by the processor 1003,or source code that may be expressed in proper format such as objectcode that is capable of being loaded into a of random access portion ofthe memory 1006 and executed by the processor 1003, etc. An executableprogram may be stored in any portion or component of the memory 506including, for example, random access memory, read-only memory, a harddrive, compact disk (CD), floppy disk, or other memory components.

The memory 1006 is defined herein as both volatile and nonvolatilememory and data storage components. Volatile components are those thatdo not retain data values upon loss of power. Nonvolatile components arethose that retain data upon a loss of power. Thus, the memory 1006 maycomprise, for example, random access memory (RAM), read-only memory(ROM), hard disk drives, floppy disks accessed via an associated floppydisk drive, compact discs accessed via a compact disc drive, magnetictapes accessed via an appropriate tape drive, and/or other memorycomponents, or a combination of any two or more of these memorycomponents. In addition, the RAM may comprise, for example, staticrandom access memory (SRAM), dynamic random access memory (DRAM), ormagnetic random access memory (MRAM) and other such devices. The ROM maycomprise, for example, a programmable read-only memory (PROM), anerasable programmable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), or other like memory device.

The processor 1003 may represent multiple processors and the memory 1006may represent multiple memories that operate in parallel. In such acase, the local interface 1009 may be an appropriate network thatfacilitates communication between any two of the multiple processors,between any processor and any one of the memories, or between any two ofthe memories etc. The processor 1003 may be of electrical, optical, ormolecular construction, or of some other construction as can beappreciated by those with ordinary skill in the art.

The operating system 1053 is executed to control the allocation andusage of hardware resources such as the memory, processing time andperipheral devices in the processor system 1000. In this manner, theoperating system 1053 serves as the foundation on which applicationsdepend as is generally known by those with ordinary skill in the art.

Experimental Validation

Data to confirm the described methodology was collected by an IdentixDFR2 100 fingerprint scanner, which uses optical scanner technology.Fingerprint images were collected from live, Play-Doh, gelatin andsilicone fingers. Live fingerprints were collected from 81 subjects,representing a wide range of ages (18-50 years), ethnicities (Caucasian,Black, and Asian), and both sexes. Some live subjects provided multiplefingerprint images during independent sessions separated by intervals ofat least two weeks. TABLE 2 summarizes the number of subjects and totalfingerprints collected using the Identix scanner. Synthetic fingers werecreated from Play-Doh, gelatin, and silicone using 38 finger casts oflive subjects.

TABLE 2 Live Play-Doh Gelatin Silicone Subjects 81 38 38 38 Sessions1-13 5 5 5 Total 644 190 190 190

Each image was processed to extract a ROI, which was used to generate ahistogram distribution. FIG. 11 illustrates typical ROIs of live andsynthetic fingerprint images from FIG. 7 and their correspondingintensity histogram distributions according to an embodiment of thepresent invention. Analysis of features A, B, and C was performed on theROI of each fingerprint image. FIG. 12 illustrates the comparison offeatures derived from the ROI histograms of all live and syntheticfingerprint images according to an embodiment of the present invention.On the x-axis of each feature set, subjects 1-644 represent livefingers, subjects 645-834 represent Play-Doh fingers, subjects 835-1024represent gelatin fingers, and subjects 1025-1214 represent siliconefingers. The histogram distribution comparison of FIG. 12 indicatesstatistical features A, B, and C may be effective for classification of“live” and “not live” fingerprints.

After the statistical features were extracted, pattern classificationtools (neural network, nearest neighbor, classification trees, andsupport vector machine) were used to separate the non-live subjects fromthe live subjects. Ten-fold cross-validation test was used to test themethod. The original sample set is partitioned into ten subsamples ofwhich nine are used as training data and one is used for validation. Thecross-validation process is then repeated ten times using differentsubsample combinations and the results were averaged to produce a singleestimation. Implementation with the different classification toolsproduced equal error rates of between 5-15%. One J48 classification treeproduced a recognition rate of 93% for synthetic subjects and 96.54% forlive subjects.

Singularity Analysis

One embodiment, among others, of liveness detection comprisesclassification analysis based upon perspiration phenomena. Thephysiological phenomenon of perspiration, observed only in live people,is discussed in “Characterization, Similarity Score and UniquenessAssociated with Perspiration Pattern,” A. Abhyankar and S. Schuckers,Lecture Notes in Computer Science, no. 3546, pp. 301-309, 2005, which ishereby incorporated by reference in its entirety.

Pre-processing of a fingerprint image involves finding the singularitypoints using wavelets analysis and transforming the information backinto the spatial domain to form a “filtered” spatial domain signal.Wavelet analysis of the images can be performed using Daubechieswavelet. Wavelet packet analysis is used to maintain scalable structureof the information content. Singularity points act as “quality check”points. Thus, bad quality and partial cadaver and synthetic images wererejected before further processing when the number of singularity pointsis below a selected threshold.

If a sufficient number of singularity points are obtained, the spatialdomain signal is subjected to Hubert Huang Technique. Wavelet packetsieving is used to tune the modes so as to gain physical significancewith reference to the evolving perspiration pattern in “live” fingers.The percentage of the energy contribution in the different modes is usedas a quantified measure to differentiate “live” fingers from “not live.”

Detection of singularity points in the fingerprint image is performedusing wavelet analysis. In one embodiment, a Daubechies wavelet isselected as the mother wavelet. Daubechies wavelets are described in TenLectures on Wavelets, 1. Daubechies, Society of Industrial and AppliedMathematics, 1998, which is hereby incorporated by reference in itsentirety. Daubechies wavelets were selected for the followingproperties:

-   -   orthonormality, having finite support;    -   optimal, compact representation of the original signal from a        sub-band coding point of view, which produces a multi-resolution        effect;    -   capability to select maximum vanishing moments and minimum        phase, so as to extract even minute details from smoother parts;        and    -   cascade algorithm allowing zoom-in on particular features of a        scaling function.

In addition, Daubechies filters can be designed so that the phase of thefilter is minimized and the number of vanishing moments are maximized.This allows extraction of changing information even from the smootherparts of an image. One skilled in the art would understand that otherwavelets could be used to determine singularities. Other methods toperform time-frequency analysis include, but are not limited to, waveletpackets, Daubechies wavelets, Harr wavelets, and short-time Fouriertransforms. Singularity point analysis is discussed in Wavelets andTheir Applications, M.-B. Ruskai, G. Beylkin, R. Coifman, I. Daubechies,S. Mallat, Y. Meyers, and L. Raphael (eds.), Jones and Bartlett, Boston,pp. 453-471, 1992, which is hereby incorporated by reference in itsentirety.

Singularity detection can be undertaken by describing the localregularity of a signal. Localizing ridge information through singularitydetection is based upon the time course of a pixel at each location (x,y) being considered a piecewise regular function. Thus, a wavelettransform that identifies singularity points at pixel locations thatexist along the temporal dimension yields a sparse representation of thedata with most coefficients close to zero. A few large coefficients areobtained at time points where the signal consists of jump-like changes(or singularities) from a baseline to an activation state or vice versa.The ability of the wavelet transform to characterize the localregularity of the ridge functions aids in this approach.

For a fingerprint image, all modulus maxima of the wavelet transform andchain maxima across scales to obtain maxima lines are computed. If theimage is noisy, maxima lines due to noise are mostly concentrated atfine scales, whereas maxima lines due to signal changes should bepersistent across coarser scales. Mathematically, singularity detectioncan be carried out by finding the abscissa where the wavelet modulusmaxima converge at finer scales.

Three criteria can be used to distinguish singularities caused by noisefluctuations from those that are generated from sharp signaltransitions, namely ridge length, ridge orientation strength, and ridgereliability. Only maxima lines that persist across all scales ofanalysis are considered as true ridge signal transitions, since noisefluctuation should have less persistence in scale-space. In addition,the strength of a ridge maxima line is computed as the sum of themodulus maxima along the line (across scales). This criterion reinforcesthe first criteria, since longer maxima lines tend to have larger valuesfor their strength, and strong signal variations yield waveletcoefficients with large amplitudes resulting in values of greatermagnitude. These criteria essentially filter out noisy and partialimages for further analysis.

FIG. 13 is an illustration of an initial (0 sec) fingerprint image and asubsequent (2 sec) fingerprint image obtained from a “live” captureaccording to an embodiment of the present invention. Singularity pointsare determined for each of the fingerprint images. TABLE 3 demonstratesthe average number of singularity points obtained from initial andsubsequent images obtained two seconds apart from a test set offingerprint images: 58 live, 50 synthetic, and 28 cadaver. The numbersgiven in TABLE 3 are averaged for each of three scanner technologies:optical, electro-optical and capacitive DC.

The singularities of the initial image and subsequent image are comparedto determine the number of matches. FIG. 14 is an illustration ofdemonstrating the linking between singularity points of the initialimage and subsequent image of FIG. 13 according to an embodiment of thepresent invention. As illustrated in TABLE 3, “live” images have alarger number of matching singularity points while noisy and “not live”images typically have fewer matching points. Singularity point detectionfilters noisy and partial images and localizes the ridge information forfurther analysis

TABLE 3 Live Synthetic Cadaver Avg. Keypoints (0 sec) 2111 1345 899 Avg.Keypoints (2 sec) 2832 1366 1341 Avg. Matches Found 932 98 167

In one embodiment, among others, if a sufficient amount of matchedsingularities are identified, then further processing of the image datais performed. As can be seen in TABLE 3, there is a detectabledifference between the number of “live” image matched singularities and“not live” image (i.e., synthetic and cadaver) matched singularities.Thus, bad quality or partial “not live” images are rejected when thenumber of singularity points is below a selected threshold. Thisminimizes processing and response time.

If the number of singularities is not equal to and/or less than thethreshold, the obtained point linking is mapped into a time domainsignal using B-spline interpolation. One skilled in the art wouldunderstand that other forms of interpolation could be used. Othermethods to perform interpolation include, but are not limited to, linearand polynomial interpolation. These signals are further processed usingan Empirical Mode Decomposition (EMD) based technique.

The Empirical Mode Decomposition (EMD) method analyzes a signal bydecomposing it into mono-components called Intrinsic Mode Functions(IMFs). The empirical nature of the approach may be partially attributedto a subjective definition of the envelope and the intrinsic modefunction involved in the sifting process. The EMD method used inconjunction with a Hilbert Transform is effective for analyzingnonlinear, non-stationary signals. This combination is known as aHilbert-Huang Transform (HHT), which is described in “The empirical modedecomposition and the hilbert spectrum for nonlinear and non-stationarytime series analysis,” N. E. Huang, S. R. Shea, M. C. Long, H. H. Wu, Q.Shih, N. C. Zheng, C. C. Yen, H. Ding, and H. Liu, Proc. R. Soc. London,vol. A-545, pp. 903-995, 1998, which is herein incorporated by referencein its entirety. Using the EMD method, the obtained signal f(t) can berepresented in terms of IMFs as:

$\begin{matrix}{{f(t)} = {{\sum\limits_{i = 1}^{n}{c_{i}(t)}} + r_{n}}} & \left( {{EQN}.\mspace{14mu} 18} \right)\end{matrix}$

where, c_(i)(t) is the i^(th) Intrinsic Mode Function and r, is theresidue.

A set of analytic functions can be constructed for these IMFs. Theanalytic function z(t) of a typical IMF c(t) is a complex signal havingthe original signal c(t) as the real part and the Hilbert transform ofthe signal c(t) as its imaginary part. By representing the signal inpolar coordinate form, one has:

z(t)=c(t)+jH[c(t)]=a(t)e ^(jφ(t)),  (EQN. 19)

where a(t) is the instantaneous amplitude and φ(t) is the instantaneousphase function. The instantaneous amplitude a(t) and the instantaneousphase function φ(t) can be calculated as:

$\begin{matrix}{{{a(t)} = \sqrt{\left\{ {c(t)} \right\}^{2} + \left\{ {H\left\lbrack {c(t)} \right\rbrack} \right\}^{2}}},{and}} & \left( {{EQN}.\mspace{14mu} 20} \right) \\{{\varphi (t)} = {\tan^{- 1}{\left\{ \frac{H\left\lbrack {c(t)} \right\rbrack}{c(t)} \right\}.}}} & \left( {{EQN}.\mspace{14mu} 21} \right)\end{matrix}$

The instantaneous frequency of a signal at time t can be expressed asthe rate of change of phase angle function of the analytic functionobtained by Hilbert Transform of the signal. The expression forinstantaneous frequency is given by:

$\begin{matrix}{{\omega (t)} = {\tan^{- 1}{\left\{ \frac{{\varphi (t)}}{t} \right\}.}}} & \left( {{EQN}.\mspace{14mu} 22} \right)\end{matrix}$

Because of the capability of extracting instantaneous amplitude a(t) andinstantaneous frequency ω(t) from the signal, this method can be used toanalyze a non-stationary signal. In the case of a single harmonicsignal, the phase angle of the Hilbert transform is a linear function oftime and the instantaneous frequency is constant and equal to thefrequency of the harmonic. In general, the concept of instantaneousfrequency describes how the signal frequency content varies with thetime.

The EMD method further decomposes the signal by sifting the IMFs. Anallowable IMF is defined as a function which satisfy following twocriterion:

-   -   The number of extrema and the number of zero crossings in the        component must either equal or differ at most by one; and    -   At any point, the mean value of the envelopes defined by the        local maxima and the local minima are zero.

The sifting process to extract IMFs from the signal processes the signaliteratively to obtain a component, which satisfies above-mentionedconditions. These constraints on the decomposed components obtain asymmetrical mono-frequency component that guarantees a well-behavedHilbert transform. A Hilbert transform behaves erratically if theoriginal function is not symmetric about the x-axis or if there is asudden change in the phase of the signal without crossing the x-axis.

Although the IMFs are well behaved in their Hilbert Transform, it maynot necessarily have any physical significance. Moreover the empiricalsifting process does not guarantee exact modal decomposition. The EMDmethod may lead to mode mixture and the signal may need to pass througha bandpass filter before analysis.

The sifting process separates the IMFs in decreasing order of frequency,i.e. it separates the high frequency component first and decomposes theresidue obtained after separating each IMP until a residue of nearlyzero frequency content is obtained. In this sense, the sifting processin the EMD method may be viewed as an implicit wavelet analysis and theconcept of the intrinsic mode function in the EMD method parallels thewavelet details in wavelet analysis.

Wavelet packet analysis of the signal also can be used as a filter bankwith adjustable time and frequency resolution. It results in symmetricalorthonormal components when a symmetrical orthogonal wavelet is used asthe decomposition wavelet. As a signal can be decomposed intosymmetrical orthonormal components with wavelet packet decomposition,this produces a well-behaved Hilbert transform. Thus, a sifting processbased on wavelet packet decomposition can be used to analyze anon-stationary signal obtained from the fingerprint images. It may beused to detect what type of fingerprint has generated the signal.

A wavelet packet is represented as a function, ψ_(j,k) ^(i) where i isthe modulation parameter, j is the dilation parameter, and k is thetranslation parameter.

ψ_(j,k) ^(i)(t)=2^(−j/2)ψ^(i)(2^(−j) −k).  (EQN. 23)

Here i=[1, 2j^(n)] and n is the level of decomposition in the waveletpacket tree. The wavelet packet coefficients c_(j,k) ^(i) correspondingto the signal f(t) can be obtained as:

c _(j,k) ^(i)(t)=∫_(−∞) ^(∞) f(t)ψ_(j,k) ^(i)(t)dt.  (EQN. 24)

The entropy E is an additive cost function that indicates the amount ofinformation stored in the signal, e.g. the higher the entropy, moreinformation is stored in the signal. Two representative definitions ofentropy include, but are not limited to, energy entropy and Shannonentropy. One skilled in the art would understand that a unique entropyfunction could be defined, if necessary.

The wavelet packet node energy entropy at a particular node n in thewavelet packet-tree of a signal is a special case of P=2 of the P-normentropy which is defined as:

$\begin{matrix}{e_{n}{\sum\limits_{k}{{c_{j,k}^{i}}^{P}{\left( {P \geq 1} \right).}}}} & \left( {{EQN}.\mspace{14mu} 25} \right)\end{matrix}$

where c_(j,k) ^(i) are the wavelet packet coefficients at a particularnode of wavelet packet tree. The wavelet packet node energy has morepotential for use in signal classification when compared to the waveletpacket node coefficients alone. The wavelet packet node energyrepresents energy stored in a particular frequency band and is mainlyused to extract the dominant frequency components of the signal. TheShannon entropy is defined as:

$\begin{matrix}{e_{n} = {- {\sum\limits_{k}{\left( c_{j,k}^{i} \right)^{2}{{\log\left\lbrack \left( c_{j,k}^{i} \right)^{2} \right\rbrack}.}}}}} & \left( {{EQN}.\mspace{14mu} 26} \right)\end{matrix}$

An entropy index (EI) is defined as a difference between the number ofzero crossings and the number of extrema in a node component of thewavelet packet tree. An entropy index value greater than 1 indicatesthat the component has a potential to reveal more information about thesignal and that it needs to be decomposed further in order to obtainadditional frequency components of the signal.

The liveness of the fingerprint is based upon the percentagecontribution of entropy energy in the sifted wavelet packet tree. Thetotal entropy energy at the sifted (or selected) wavelet packet nodes iscalculated and compared to a predetermined threshold. If the thresholdis met or exceeded, then the fingerprint is classified as “live.”

Referring next to FIG. 15, shown is a flow chart 1500 that provides oneexample of the operation of a system to determine liveness offingerprint images such as those illustrated in FIG. 13 according to anembodiment of the present invention. Alternatively, the flow chart ofFIG. 15 may be viewed as depicting steps of an example of a methodimplemented in a processor system 1600 (FIG. 16) to determine livenessof fingerprint images as set forth above. The functionality of thefingerprint liveness detection as depicted by the example flow chart ofFIG. 15 may be implemented, for example, in an object oriented design orin some other programming architecture. Assuming the functionality isimplemented in an object oriented design, then each block representsfunctionality that may be implemented in one or more methods that areencapsulated in one or more objects. The fingerprint liveness detectionmay be implemented using any one of a number of programming languagessuch as, for example, C, C++, or other programming languages.

Beginning with block 1510, First and second fingerprint images arereceived. The images are sequentially obtained from the same fingerprintusing a predetermined time delay. In one embodiment, such as thatillustrated in FIG. 13, the second image is obtained two seconds afterthe first image. In another embodiment, a delay of five seconds is used.

Singularity point detection is performed in block 1520. Singularitypoint detection can be performed on the first and second fingerprintimages. The singularities are compared between the two images todetermine the number of matches. Live images have a larger number ofmatching singularity points. Noisy and “not live” images typically havefewer matching points. Singularity point detection filters noisy andpartial images and localizes the ridge information for further analysis.

The localized ridge information at the matched singularity points isinterpolated in block 1530 to provide time domain signals. Interpolationcan be accomplished using cubic splines or other appropriate methods.The interpolated data increases the time resolution of the signal, whichwill in turn increase the regularity of the decomposed components. Useof cubic spline interpolation assures the conservation of signal databetween sampled points without large oscillations.

In block 1540, the interpolated data is decomposed into differentfrequency components using wavelet packet decomposition. The shape ofthe decomposed components produced by wavelet analysis depends on theshape of the mother wavelet used for the decomposition. Daubechieswavelet of higher order (e.g., 16) has good symmetry, which leads tosymmetrical and regular shaped components. One skilled in the art wouldunderstand that other wavelets may be used in the decomposition.

In the case of a binary wavelet packet tree, decomposition at level nresults in 2^(n) components. This number may become very large at higherdecomposition levels and necessitate increased computational efforts. Animproved decomposition of the signal can be achieved using similarconditions to that required for an IMF. In this embodiment, a particularnode (N) is split into two nodes N₁ and N₂ only if the entropy index ofthe corresponding node is greater than 1. Thus, the entropy of thewavelet packet decomposition is kept as minimal as possible. Othercriteria such as, but not limited to, the minimum number of zerocrossings and the minimum peak value of components can also be appliedto decompose only the potential components in the signal. One skilled inthe art would understand that decomposition could be unique if themother wavelet in the wavelet packet analysis is defined and the siftingcriteria are specified.

Once the decomposition is carried out, the entropy energies aredetermined from the components (block 1550) corresponding to the nodesof the wavelet packet tree. In block 1560, the percentage of the entropyenergy contribution of the component corresponding to each node to theoriginal signal can be used as the sifting criteria in order to identifyappropriate signal components. This can be obtained by summing up theenergy entropy corresponding to the terminal nodes of the wavelet packettree of the signal decomposition to get total energy content. Then, thepercentage contribution of entropy energy corresponding to each terminalnode is calculated. The higher the contribution percentage, the moresignificant the component is. Sifting is based upon exceeding apredefined contribution percentage.

In other embodiments, the decomposed components of the signal can besifted (block 1560) prior to determining the entropy energy in block1550. In those embodiments, the decomposed signals can be sifted usingthe same criteria applied to IMFs. Other sifting criteria may also beutilized.

The liveness of the fingerprint is determined in block 1570. Thepercentage contribution of entropy energy in the wavelet packet tree atsifted (or selected) wavelet packet nodes is calculated and compared toa predetermined threshold. If the threshold is met or exceeded, then thefingerprint is classified as “live.”

Referring next to FIG. 16, shown is one example of a system thatperforms various functions using fingerprint liveness detectionaccording to the various embodiments as set forth above. As shown, aprocessor system 1600 is provided that includes a processor 1603 and amemory 1606, both of which are coupled to a local interface 1609. Thelocal interface 1609 may be, for example, a data bus with anaccompanying control/address bus as can be appreciated by those withordinary skill in the art. The processor system 1600 may comprise, forexample, a computer system such as a server, desktop computer, laptop,personal digital assistant, or other system with like capability.

Coupled to the processor system 1600 are various peripheral devices suchas, for example, a display device 1613, a keyboard 1619, and a mouse1623. In addition, other peripheral devices that allow for the captureof various patterns may be coupled to the processor system 1600 such as,for example, an image capture device 1626, or a biometric input device1629. The image capture device 1626 may comprise, for example, a digitalcamera or other such device that generates images that comprise patternsto be analyzed as described above. Also, the biometric input device 1629may comprise, for example, a fingerprint input device, optical scanner,or other biometric device 1629 as can be appreciated.

Stored in the memory 1606 and executed by the processor 1603 are variouscomponents that provide various functionality according to the variousembodiments of the present invention. In the example embodiment shown,stored in the memory 1606 is an operating system 1653 and a fingerprintliveness detection system 1656. In addition, stored in the memory 1606are various images 1659 and various decomposed time signals 1663. Thedecomposed signals 1663 may be associated with corresponding ones of thevarious images 1659. The images 1659 and the decomposed signals 1663 maybe stored in a database to be accessed by the other systems as needed.The images 1659 may comprise fingerprints such as the images in FIG. 13or other patterns as can be appreciated. The images 1659 comprise, forexample, a digital representation of physical patterns or digitalinformation such as data, etc.

The fingerprint liveness detection system 1656 is executed by theprocessor 1603 in order to classify whether a fingerprint image is“live” or “not live” as described above. A number of software componentsare stored in the memory 1606 and are executable by the processor 1603.In this respect, the term “executable” means a program file that is in aform that can ultimately be run by the processor 1603. Examples ofexecutable programs may be, for example, a compiled program that can betranslated into machine code in a format that can be loaded into arandom access portion of the memory 1606 and run by the processor 1603,or source code that may be expressed in proper format such as objectcode that is capable of being loaded into a of random access portion ofthe memory 1606 and executed by the processor 1603, etc. An executableprogram may be stored in any portion or component of the memory 1606including, for example, random access memory, read-only memory, a harddrive, compact disk (CD), floppy disk, or other memory components.

The memory 1606 is defined herein as both volatile and nonvolatilememory and data storage components. Volatile components are those thatdo not retain data values upon loss of power. Nonvolatile components arethose that retain data upon a loss of power. Thus, the memory 1606 maycomprise, for example, random access memory (RAM), read-only memory(ROM), hard disk drives, floppy disks accessed via an associated floppydisk drive, compact discs accessed via a compact disc drive, magnetictapes accessed via an appropriate tape drive, and/or other memorycomponents, or a combination of any two or more of these memorycomponents. In addition, the RAM may comprise, for example, staticrandom access memory (SRAM), dynamic random access memory (DRAM), ormagnetic random access memory (MRAM) and other such devices. The ROM maycomprise, for example, a programmable read-only memory (PROM), anerasable programmable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), or other like memory device.

The processor 1603 may represent multiple processors and the memory 1606may represent multiple memories that operate in parallel. In such acase, the local interface 1609 may be an appropriate network thatfacilitates communication between any two of the multiple processors,between any processor and any one of the memories, or between any two ofthe memories etc. The processor 1603 may be of electrical, optical, ormolecular construction, or of some other construction as can beappreciated by those with ordinary skill in the art.

The operating system 1653 is executed to control the allocation andusage of hardware resources such as the memory, processing time andperipheral devices in the processor system 1600. In this manner, theoperating system 1653 serves as the foundation on which applicationsdepend as is generally known by those with ordinary skill in the art.

Experimental Validation

The described technique was applied to fingerprint images captured at 0and after 2 sec from three finger categories: 58 live; 28 cadaver; and50 synthetic. EMD decomposition was applied to each image for fourmodes. The described technique was applied to the obtained fingerprintimages to determine singularity points. B-spline interpolation was usedto produce time domain signals, which were decomposed using Daubechieswavelets. FIG. 17 illustrates EMD decomposition of (a) live and (b)synthetic fingerprints according to an embodiment of the presentinvention.

The average percentage contribution of energy in wavelet packet tree atmost common packet nodes was calculated for the second, third, andfourth modes for each type of fingerprint scanner. TABLES 4, 5, and 6show the average percentage contribution of entropy energy forEthentica, Secugen, and Precise Biometrics scanners, respectively.Classification rates of 93%, 98.9% and 89.2% were achieved for Secugen,Precise Biometrics and Ethentica, respectively.

TABLE 4 Mode No. Packet Node Live Synthetic Cadaver 2 (8, 1) 52.2 6.33.9 3 (7, 0) 61.3 11.2 14.8 4 (6, 1) 91.2 57.3 37.3

TABLE 5 Mode No. Packet Node Live Synthetic Cadaver 2 (8, 1) 46.5 12.20.6 3 (7, 0) 71.3 22 5.5 4 (6, 1) 77.2 28.7 41.2

TABLE 6 Mode No. Packet Node Live Synthetic Cadaver 2 (8, 1) 31.2 2 3.13 (7, 0) 60 19.9 32.6 4 (6, 1) 86.5 44.3 56.4

Although the fingerprint liveness detection systems 556, 1056, and/or1656 are described as being embodied in software or code executed bygeneral purpose hardware as discussed above, as an alternative the samemay also be embodied in dedicated hardware or a combination ofsoftware/general purpose hardware and dedicated hardware. If embodied indedicated hardware, each of the fingerprint liveness detection systems556, 1056, and/or 1656 can be implemented as a circuit or state machinethat employs any one of or a combination of a number of technologies.These technologies may include, but are not limited to, discrete logiccircuits having logic gates for implementing various logic functionsupon an application of one or more data signals, application specificintegrated circuits having appropriate logic gates, programmable gatearrays (PGA), field programmable gate arrays (FPGA), or othercomponents, etc. Such technologies are generally well known by thoseskilled in the art and, consequently, are not described in detailherein.

The flow charts of FIGS. 5, 10, and 16 show the architecture,functionality, and operation of an implementation of the fingerprintliveness detection systems 556, 1056, and/or 1656. If embodied insoftware, each block may represent a module, segment, or portion of codethat comprises program instructions to implement the specified logicalfunction(s). The program instructions may be embodied in the form ofsource code that comprises human-readable statements written in aprogramming language or machine code that comprises numericalinstructions recognizable by a suitable execution system such as aprocessor in a computer system or other system. The machine code may beconverted from the source code, etc. If embodied in hardware, each blockmay represent a circuit or a number of interconnected circuits toimplement the specified logical function(s).

Although flow charts of FIGS. 5, 10, and 16 show a specific order ofexecution, it is understood that the order of execution may differ fromthat which is depicted. For example, the order of execution of two ormore blocks may be scrambled relative to the order shown. Also, two ormore blocks shown in succession in FIGS. 5, 10, and 16 may be executedconcurrently or with partial concurrence. In addition, any number ofcounters, state variables, warning semaphores, or messages might beadded to the logical flow described herein, for purposes of enhancedutility, accounting, performance measurement, or providingtroubleshooting aids, etc. It is understood that all such variations arewithin the scope of the present invention.

Also, where each of the fingerprint liveness detection systems 556,1056, and/or 1656 may comprise software or code, each can be embodied inany computer-readable medium for use by or in connection with aninstruction execution system such as, for example, a processor in acomputer system or other system. In this sense, the logic may comprise,for example, statements including instructions and declarations that canbe fetched from the computer-readable medium and executed by theinstruction execution system. In the context of the present invention, a“computer-readable medium” can be any medium that can contain, store, ormaintain the fingerprint liveness detection systems 556, 1056, and/or1656 for use by or in connection with the instruction execution system.The computer readable medium can comprise any one of many physical mediasuch as, for example, electronic, magnetic, optical, electromagnetic,infrared, or semiconductor media. More specific examples of a suitablecomputer-readable medium would include, but are not limited to, magnetictapes, magnetic floppy diskettes, magnetic hard drives, or compactdiscs. Also, the computer-readable medium may be a random access memory(RAM) including, for example, static random access memory (SRAM) anddynamic random access memory (DRAM), or magnetic random access memory(MRAM). In addition, the computer-readable medium may be a read-onlymemory (ROM), a programmable read-only memory (PROM), an erasableprogrammable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), or other type of memory device.

It should be emphasized that the above-described embodiments of thepresent invention are merely possible examples of implementations,merely set forth for a clear understanding of the principles of theinvention. Many variations and modifications may be made to theabove-described embodiment(s) of the invention without departingsubstantially from the spirit and principles of the invention. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and the present invention and protected bythe following claims.

1. A method for determining fingerprint liveness, comprising the stepsof: receiving a fingerprint image; extracting a region of interest fromthe fingerprint image; generating a histogram of the region of interest;determining a statistical feature of the histogram; and determiningliveness of the fingerprint image based upon the statistical feature. 2.The method of claim 1, wherein the histogram is based upon grayscalevariation of pixels within the region of interest.
 3. The method ofclaim 1, wherein the step of determining a statistical feature of thehistogram further comprises: summing histogram distribution values overa predefined range of the histogram; and normalizing the sum ofhistogram distribution values.
 4. The method of claim 1, wherein thestep of determining liveness of the fingerprint image further comprisesperforming a neural network analysis of the statistical feature.
 5. Themethod of claim 1, wherein the step of determining liveness of thefingerprint image further comprises performing a classification treeanalysis of the statistical feature.
 6. The method of claim 5, whereinthe classification tree analysis is based upon J48 classification. 7.The method of claim 1, wherein the step of extracting a region ofinterest further comprises the steps of: dividing the fingerprint imageinto blocks; determining a variance for each image block; removing imageblocks based upon the variance and a predetermined threshold;determining a center point of the remaining image; and extracting aregion of interest about the center point.
 8. The method of claim 7,wherein the variance is based upon intensity variation of pixels withineach image block.
 9. The method of claim 7, wherein the region ofinterest is a predefined area centered at the center point.
 10. Themethod of claim 9, wherein the predefined area is square.
 11. A system,comprising: a processor circuit having a processor and a memory; adetection system stored in the memory and executable by the processor,the detection system comprising: logic that receives a fingerprintimage; logic that extracts a region of interest from the fingerprintimage; logic that generates a histogram of the region of interest; logicthat determines a statistical feature of the histogram; and logic thatdetermines liveness of the fingerprint image based upon the statisticalfeature.
 12. The system of claim 11, wherein the logic that extracts aregion of interest further comprises: logic that divides the fingerprintimage into blocks; logic that determines a variance for each imageblock; logic that removes image blocks based upon the variance and apredetermined threshold; logic that determines a center point of theremaining image; and logic that extracts a region of interest about thecenter point.
 13. The system of claim 12, wherein the variance is basedupon intensity variation of pixels within each image block.
 14. Thesystem of claim 11, wherein the logic that determines a statisticalfeature of the histogram further comprises: logic that sums histogramdistribution values over a predefined range of the histogram; and logicthat normalizes the sum of histogram distribution values.
 15. The systemof claim 11, wherein the logic that determines liveness of thefingerprint image further comprises logic to perform a classificationtree analysis of the statistical feature.
 16. A system, comprising:means for receiving a fingerprint image; means for extracting a regionof interest from the fingerprint image; means for generating a histogramof the region of interest; means for determining a statistical featureof the histogram; and means for determining liveness of the fingerprintimage based upon the statistical feature.
 17. The method of claim 16,wherein the means for extracting a region of interest further comprises:means for dividing the fingerprint image into blocks; means fordetermining a variance for each image block; means for removing imageblocks based upon the variance and a predetermined threshold; means fordetermining a center point of the remaining image; and means forextracting a region of interest about the center point.
 18. The systemof claim 16, wherein the means for determining a statistical feature ofthe histogram further comprises: means for summing histogramdistribution values over a predefined range of the histogram; and meansfor normalizing the sum of histogram distribution values.
 19. The systemof claim 16, wherein the means for determining liveness of thefingerprint image further comprises means for performing aclassification tree analysis of the statistical feature.